In 1995, the American Psychology Association (APA), through its Task Force for the analysis of the concept Intelligence, considered it as the ability to “understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought”.

The question we must ask when relating this concept to that of smart cities and big data & analytics is if asking the right questions of data, so that they assist us in making decisions, can help us to better adapt to the environment, learn from experience and engage in various forms of reasoning.

The answer will gradually emerge but, undoubtedly, having data that professionals trained in the so-called STEM fields obtain and process will in its turn also require professionals from various fields who know what to ask of the data. In this case, perhaps the question is more important than finding answers, which technically seems increasingly simpler.

« A city cannot be smart unless it takes human factors into account »

Smart cities must form part of a whole with the citizens who live there, who must be served by technology to develop their maximum potential. A city cannot be smart unless it takes human factors into account; and we are not referring to those related to person-computer interaction or to the study of the user’s experience. We are referring to the psychological, social, anthropological, ethical and cultural variables and, in short, to everything related to “the human”.

The key lies in the intersection between these disciplines and the technological knowledge that traditionally has not been the object of its study but that is now essential to understand the world in which we live. So a training that combines behavioural sciences and social sciences with knowledge of technology will be the norm, not the exception.

The importance of the human, at any of its levels — individual, social, cultural, etc. — is already taken into account in companies like Google, which regularly employs professionals from the humanities, and not only experts in technology as one might suppose.

They do so because they need to work with emotions, habits, attitudes and individual differences in fields such as gender, intelligence, creativity, empathy and, in short, with everything that makes us humans. Perhaps in addition to STEM workers, we will also need workers from PSAEC (Psychology, Sociology, Anthropology, Ethics, and Culture).

« A training that combines behavioural sciences and social sciences with knowledge of technology will be the norm, not the exception »

For example, who else will analyse how we will be psychologically affected by the realization that technology will make it possible, at least potentially, to know the smallest detail of our lifestyle? To what extent is this ethical? What psychological and also social effects will the interaction with robots that look like and “react” increasingly more like “humans” have? Will we fall in love like in the film HER? Will we hate artificial beings that at any moment can give us the response or generate the emotion we need?

How will we know what is relevant and what not to “ask” of the data to separate the essential from the superfluous for people? Who must do this and what training must they have? We firmly believe that it will be PSAEC professionals who, by definition, will also incorporate technological experience into their training that will enable them to do essential interdisciplinary work, but sometimes less usual than we imagine.

A final example. Do you know which professional is specifically discussed on the APA website? It is the big data psychologist, whose mission, like that of other professionals, will be to go beyond compiling data and to make sense of it to resolve human problems. Only in this way, by seeking the meaning of the collection and analysis of big data, will a smart city also be a human city.

Every day we generate a huge amount of big data, but we need to resort to analytics to make abstract information meaningful and get valuable knowledge from it. In education, learning platforms let us easily gather an immense quantity of data regarding students’ behaviour, interactions, preferences and opinions. When properly analysed — through learning analytics — all these data might provide useful insight on how to make learning processes more adaptive, attractive and efficient.

Are these techniques allowing us to provide better support to our students? Are we taking advantage of big data and analytics to help shape the citizens of the future?